Alphabet Learning Media Using Image Classification for Speech-Impaired Students in Special Education Schools
Abstract
This research aims to develop an image classification-based learning medium for teaching the alphabet to students with speech impairments in special schools (SLB). The technique used in image classification is Random Forest with a dataset of 5,400 images, including 1 default image and 26 alphabet classes. The software development follows the waterfall model, including requirements analysis, system design, implementation, and testing, with system design utilizing object-oriented analysis and design (OOAD). Evaluation metrics, including accuracy (100.00%), precision (1.00), recall (1.00), and F1 score (1.00), indicate the model’s outstanding performance. The system was tested on 10 students with speech impairments, showing an average improvement in ability from 5.9 in the pretest to 12.8 in the posttest, demonstrating consistent gains among participants. This image classification-based learning medium is expected to support the learning process for students with speech impairments in SLB effectively
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